Journal of Computational Neuroscience

, Volume 43, Issue 1, pp 5–15 | Cite as

Predictive control of intersegmental tarsal movements in an insect

  • Alicia Costalago-Meruelo
  • David M. Simpson
  • Sandor M. Veres
  • Philip L. Newland
Article
  • 161 Downloads

Abstract

In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.

Keywords

Reflex Artificial Neural Network Metaheuristic algorithm Evolutionary programming Particle swarm optimisation Locust Motor control 

Notes

Acknowledgements

Alicia Costalago-Meruelo was supported by an EPRSC grant (EP/G03690X/1) from The Institute of Sound and Vibration Research and the Institute for Complex Systems Simulations at the University of Southampton. The data is freely available through the Southampton University repository under. doi: 10.5258/SOTON/D0014.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

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References

  1. Angarita-Jaimes, N., Dewhirst, O. P., Simpson, D. M., Kondoh, Y., Allen, R., & Newland, P. L. (2012). The dynamics of analogue signalling in local networks controlling limb movement. European Journal of Neuroscience, 36(9), 3269–3282.CrossRefPubMedGoogle Scholar
  2. Angeline, P. J., Saunders, G. M., & Pollack, J. B. (1994). An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5(1), 54–65.CrossRefPubMedGoogle Scholar
  3. Au, S. K., & Herr, H. M. (2008). Powered ankle-foot prosthesis. IEEE Robotics and Automation Magazine, 15(3), 52–59.CrossRefGoogle Scholar
  4. Bares, J. E. (1999). Dante II: technical description, results, and lessons learned. The International Journal of Robotics Research, 18(7), 621–649.CrossRefGoogle Scholar
  5. Beer, R. D., Quinn, R. D., Chiel, H. J., & Ritzmann, R. E. (1997). Biologically inspired approaches to robotics: what can we learn from insects? Communications of the ACM, 40(3), 30–38.CrossRefGoogle Scholar
  6. Bishop, C. M., Lange, N., & Ripley, B. D. (1995). Neural networks for pattern recognition (Vol. 92). London: Oxford University Press.Google Scholar
  7. Burrows, M. (1996). The neurobiology of an insect brain. Oxford: Oxford University Press.CrossRefGoogle Scholar
  8. Burrows, M., & Horridge, G. A. (1974). The organization of inputs to motoneurons of the locust metathoracic leg. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences, 269(896), 49–94.CrossRefPubMedGoogle Scholar
  9. Büschges, A., & Gruhn, M. (2007). Mechanosensory feedback in walking: from joint control to locomotor patterns. In Insect mechanics and control (Vol. 34, pp. 193–230). Academic Press.Google Scholar
  10. Büschges, A., Kittmann, R., & Schmitz, J. (1994). Identified nonspiking interneurons in leg reflexes and during walking in the stick insect. Journal of Comparative Physiology A, 174(6), 685–700.CrossRefGoogle Scholar
  11. Chen, D., Yin, J., Zhao, K., Zheng, W., & Wang, T. (2011). Bionic mechanism and kinematics analysis of hopping robot inspired by locust jumping. Journal of Bionic Engineering, 8(4), 429–439.CrossRefGoogle Scholar
  12. Clarac, F., Vedel, J. P., & Bush, B. M. (1978). Intersegmental reflex coordination by a single joint receptor organ (CB) in rock lobster walking legs. The Journal of Experimental Biology, 73, 29–46.PubMedGoogle Scholar
  13. Costalago Meruelo, A., Simpson, D. M., Veres, S. M., & Newland, P. L. (2016). Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron. Neural Networks, 75, 56–65.CrossRefPubMedGoogle Scholar
  14. Cruse, H., Dautenhahn, K., & Schreiner, H. (1992). Coactivation of leg reflexes in the stick insect. Biological Cybernetics, 67(4), 369–375.CrossRefGoogle Scholar
  15. Cruse, H., Kindermann, T., Schumm, M., Dean, J., & Schmitz, J. (1998). Walknet—a biologically inspired network to control six-legged walking. Neural Networks, 11(7–8), 1435–1447.CrossRefPubMedGoogle Scholar
  16. Delcomyn, F. (2004). Insect walking and robotics. Annual Review of Entomology, 49, 51–70.CrossRefPubMedGoogle Scholar
  17. Delcomyn, F., & Nelson, M. E. (2000). Architectures for a biomimetic hexapod robot. Robotics and Autonomous Systems, 30(1), 5–15.CrossRefGoogle Scholar
  18. Dewhirst, O. P. (2012). Nonlinear system analysis of local reflex control of locust hind limbs by, PhD thesis, University of Southampton.Google Scholar
  19. Dewhirst, O. P., Angarita-Jaimes, N., Simpson, D. M., Allen, R., & Newland, P. L. (2013). A system identification analysis of neural adaptation dynamics and nonlinear responses in the local reflex control of locust hind limbs. Journal of Computational Neuroscience, 34(1), 39–58.CrossRefPubMedGoogle Scholar
  20. Dürr, V., Schmitz, J., & Cruse, H. (2004). Behaviour-based modelling of hexapod locomotion: linking biology and technical application. Arthropod Structure and Development, 33(3), 237–250.CrossRefPubMedGoogle Scholar
  21. Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. Springer Science & Business Media.Google Scholar
  22. Endo, W., Santos, F. P., Simpson, D., Maciel, C. D., & Newland, P. L. (2015). Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network. Journal of Computational Neuroscience, 38(2), 427–438.CrossRefPubMedGoogle Scholar
  23. Espenschied, K. S., Chiel, H. J., Quinn, R. D., & Beer, R. D. (1993). Leg coordination mechanisms in the stick insect applied to hexapod robot locomotion. Adaptive Behavior, 1(4), 455–468.CrossRefGoogle Scholar
  24. Espenschied, K. S., Quinn, R. D., Beer, R. D., & Chiel, H. J. (1996). Biologically based distributed control and local reflexes improve rough terrain locomotion in a hexapod robot. Robotics and Autonomous Systems, 18(1–2), 59–64.CrossRefGoogle Scholar
  25. Faisal, A., Selen, L. P. J., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews. Neuroscience, 9(4), 292–303.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Field, L. H., & Burrows, M. (1982). Reflex effects of the femoral chordotonal organ upon leg motor neurones of the locust. Journal of Experimental Biology, 101(1), 265–285.Google Scholar
  27. Field, L. H., & Rind, F. C. (1981). A single insect chordotonal organ mediates inter-and intra-segmental leg reflexes. Comparative Biochemistry and Physiology Part A, 68(1), 99–102.CrossRefGoogle Scholar
  28. Gandevia, S. C., Refshauge, K. M., & Collins, D. F. (2002). Proprioception: peripheral inputs and perceptual interactions BT - sensorimotor control of movement and posture. Boston: Springer.Google Scholar
  29. Goble, D. J., Coxon, J. P., Wenderoth, N., Van Impe, A., & Swinnen, S. P. (2009). Proprioceptive sensibility in the elderly: degeneration, functional consequences and plastic-adaptive processes. Neuroscience and Biobehavioral Reviews, 33(3), 271–278.Google Scholar
  30. Halbertsma, J. M. (1983). The stride cycle of the cat: the modelling of locomotion by computerized analysis of automatic recordings. Acta Physiologica Scandinavica. Supplementum, 521, 1–75.PubMedGoogle Scholar
  31. Hanson, M. A., Burton, A. K., Kendall, N. A. S., Lancaster, R. J., & Pilkington, A. (2006). The costs and benefits of active case management and rehabilitation for musculoskeletal disorders, Prepared by Hu-Tech Associates Ltd for the Health and Safety Executive, London, 2006.Google Scholar
  32. Haykin, S. (2004). Neural networks: a comprehensive foundation (Vol. 2). Englewood Cliffs: Prentice Hall.Google Scholar
  33. He, J., Maltenfort, M., Wang, Q. W. Q., & Hamm, T. (2001). Learning from biological systems: modeling neural control. IEEE Control Systems Magazine, 21(4), 55–69.CrossRefGoogle Scholar
  34. Ijspeert, A. J. (2008). Central pattern generators for locomotion control in animals and robots: a review. Neural Networks, 21(4), 642–653.CrossRefPubMedGoogle Scholar
  35. Jiménez-Fabián, R., & Verlinden, O. (2012). Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Medical Engineering and Physics, 34(4), 397–408.CrossRefPubMedGoogle Scholar
  36. John, H. (1992). Holland, Adaptation in natural and artificial systems. Cambridge: MIT Press.Google Scholar
  37. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95—international conference on neural networks. (Vol. 4, pp. 1942–1948). IEEE.Google Scholar
  38. Kondoh, Y., Okuma, J., & Newland, P. L. (1995). Dynamics of neurons controlling movements of a locust hind leg: Wiener kernel analysis of the responses of proprioceptive afferents. Journal of Neurophysiology, 73(5), 1829–1842.PubMedGoogle Scholar
  39. Kovač, M., Fuchs, M., Guignard, A., Zufferey, J. C., & Floreano, D. (2008). A miniature 7g jumping robot. In Proceedings—IEEE international conference on robotics and automation (pp. 373–378).Google Scholar
  40. Lewinger, W. A., Reekie, H. M., & Webb, B. (2011). A hexapod robot modeled on the stick insect. In IEEE 15th international conference on advanced robotics: new boundaries for robotics (pp. 541–548). ICAR 2011’.Google Scholar
  41. Ljung, L. (1998). System identification. In Signal analysis and prediction (pp. 163–173). Springer.Google Scholar
  42. Marder, E., & Taylor, A. L. (2011). Multiple models to capture the variability in biological neurons and networks. Nature Neuroscience, 14(2), 133–138.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Marmarelis, V. Z. (2004). Nonlinear dynamic modeling of physiological systems (Vol. 10). New York: Wiley.CrossRefGoogle Scholar
  44. Newland, P. L., & Kondoh, Y. (1997a). Dynamics of neurons controlling movements of a locust hind leg II. Flexor tibiae motor neurons. Journal of Neurophysiology, 77(4), 1731–1746.Google Scholar
  45. Newland, P. L., & Kondoh, Y. (1997b). Dynamics of neurons controlling movements of a locust hind leg. III. Extensor tibiae motor neurons. Journal of Neurophysiology, 77(6), 3297–3310.Google Scholar
  46. Pearson, K. G. (1993). Common principles of motor control in vertebrates and invertebrates. Annual Review of Neuroscience, 16, 265–297.CrossRefPubMedGoogle Scholar
  47. Pearson, K. G. (1995). Proprioceptive regulation of locomotion. Current Opinion in Neurobiology, 5(6), 786–791.CrossRefPubMedGoogle Scholar
  48. Ritzmann, R. E., & Büschges, A. (2007). Adaptive motor behavior in insects. Current Opinion in Neurobiology, 17(6), 629–636.CrossRefPubMedGoogle Scholar
  49. Ritzmann, R. E., Quinn, R. D., & Fischer, M. S. (2004). Convergent evolution and locomotion through complex terrain by insects, vertebrates and robots. Arthropod Structure and Development, 33(3), 361–379.CrossRefPubMedGoogle Scholar
  50. Rushton, D. N. (1997). Functional electrical stimulation. Physiological Measurements, 18(4), 241–75.CrossRefGoogle Scholar
  51. Schneidman, E., Brenner, N., Tishby, N., van Steveninck, R. R. D. R., & Bialek, W. (2000). Universality and individuality in a neural code. ArXiv Physics e-prints p. 16.Google Scholar
  52. Shultz, A. H., Lawson, B. E., & Goldfarb, M. (2016). Variable cadence walking and ground adaptive standing with a powered ankle prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (4), 495–505.CrossRefPubMedGoogle Scholar
  53. Sietsma, J., & Dow, R. J. F. (1991). Creating artificial neural networks that generalize. Neural Networks, 4 (1), 67–79.CrossRefGoogle Scholar
  54. Stewart, J. D. (2008). Foot drop: where, why and what to do? Practical Neurology, 8(3), 158–169.CrossRefPubMedGoogle Scholar
  55. Suraweera, N. P., & Ranasinghe, D. N. (2008). A natural algorithmic approach to the structural optimisation of neural networks. In Proceedings of the 2008 4th international conference on information and automation for sustainability (pp. 150–156). ICIAFS 2008.Google Scholar
  56. Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989). Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing, 37(3), 328–339.CrossRefGoogle Scholar
  57. Webb, B. (2002). Robots in invertebrate neuroscience. Nature, 417(6886), 359–363.CrossRefPubMedGoogle Scholar
  58. Webb, B, Harrison, R. R., & Willis, M. A. (2004). Sensorimotor control of navigation in arthropod and arti cial systems.Google Scholar
  59. Yao, X. (1999). Evolving artificial neural networks. In Proceedings of the IEEE (Vol. 87, pp. 1423–1447).Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Alicia Costalago-Meruelo
    • 1
    • 4
  • David M. Simpson
    • 1
  • Sandor M. Veres
    • 2
  • Philip L. Newland
    • 3
  1. 1.Faculty of Engineering and the EnvironmentUniversity of SouthamptonSouthamptonUK
  2. 2.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
  3. 3.Biological SciencesUniversity of SouthamptonSouthamptonUK
  4. 4.Neurologisches ForschungshausLudwig-Maximilians-UniversitätMünchenGermany

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